Ji He

Artificial Intelligence · with Li · 2015

A researcher in the Science corpus: 3 papers; trusts 16 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 41 citations into it; #17,533 of 35,345 by trust here, #10,207 by citations
• Reinforcement Learning — 37 citations into it; #1,290 of 2,242 by trust here, #876 by citations
• Natural Language Processing — 28 citations into it; #3,316 of 5,688 by trust here, #2,660 by citations

Whom Ji He trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Geoffrey E. Hinton
2. Volodymyr Mnih
3. Longxin Lin
4. G. Tesauro
5. Christopher J. C. H. Watkins
6. Richard S. Sutton
7. Yoshua Bengio
8. Alex Krizhevsky
9. Ilya Sutskever
10. Karthik Narasimhan

Who trusts Ji He overall

1. Prithviraj Ammanabrolu
2. Matthew J. Hausknecht
3. Yuxi Li
4. Shunyu Yao
5. Karthik Narasimhan
6. Brielen Madureira
7. Vincent Micheli
8. Mark O. Riedl
9. Dipendra Kumar Misra
10. Rodrigo Nogueira

Papers from Ji He's perspective

Ordered by how strongly the researchers Ji He trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Deep Reinforcement Learning with a Natural Language Action Space (2015) — 268 citations
• Recurrent Reinforcement Learning: A Hybrid Approach (2015) — 77 citations
• Deep Reinforcement Learning with an Unbounded Action Space (2015) — 19 citations